Overview

Brought to you by YData

Dataset statistics

Number of variables22
Number of observations50000
Missing cells254230
Missing cells (%)23.1%
Total size in memory8.8 MiB
Average record size in memory184.0 B

Variable types

Text14
Numeric8

Alerts

performance_title has 2215 (4.4%) missing valuesMissing
end_time has 35221 (70.4%) missing valuesMissing
postcode has 3635 (7.3%) missing valuesMissing
capacity has 27015 (54.0%) missing valuesMissing
venue_address has 28927 (57.9%) missing valuesMissing
latitude has 1904 (3.8%) missing valuesMissing
longitude has 1904 (3.8%) missing valuesMissing
duration_mins has 8442 (16.9%) missing valuesMissing
transaction_datetime has 23299 (46.6%) missing valuesMissing
audience_segment has 24378 (48.8%) missing valuesMissing
audience_subsegment has 24378 (48.8%) missing valuesMissing
membership_type has 43695 (87.4%) missing valuesMissing
has_negative_tickets has 28916 (57.8%) missing valuesMissing
ticket_count is highly skewed (γ1 = 33.44284555)Skewed
has_negative_tickets is highly skewed (γ1 = 22.89490082)Skewed
month is highly skewed (γ1 = -20.08807464)Skewed
has_negative_tickets has 21044 (42.1%) zerosZeros

Reproduction

Analysis started2025-11-02 20:43:26.107992
Analysis finished2025-11-02 20:43:27.102663
Duration0.99 seconds
Software versionydata-profiling vv4.17.0
Download configurationconfig.json

Variables

Distinct108
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size781.2 KiB
2025-11-02T20:43:27.404211image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters500000
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2019-08-17
2nd row2017-08-28
3rd row2017-08-19
4th row2024-08-21
5th row2024-08-21
ValueCountFrequency (%)
2024-08-251987
 
4.0%
2024-08-181689
 
3.4%
2024-08-241602
 
3.2%
2024-08-171466
 
2.9%
2024-08-231410
 
2.8%
2024-08-041389
 
2.8%
2024-08-161310
 
2.6%
2024-08-101305
 
2.6%
2024-08-031278
 
2.6%
2024-08-061273
 
2.5%
Other values (98)35291
70.6%
2025-11-02T20:43:27.863396image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0118367
23.7%
-100000
20.0%
297196
19.4%
860996
12.2%
146467
 
9.3%
432813
 
6.6%
713009
 
2.6%
912555
 
2.5%
57256
 
1.5%
36194
 
1.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)500000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0118367
23.7%
-100000
20.0%
297196
19.4%
860996
12.2%
146467
 
9.3%
432813
 
6.6%
713009
 
2.6%
912555
 
2.5%
57256
 
1.5%
36194
 
1.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)500000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0118367
23.7%
-100000
20.0%
297196
19.4%
860996
12.2%
146467
 
9.3%
432813
 
6.6%
713009
 
2.6%
912555
 
2.5%
57256
 
1.5%
36194
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)500000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0118367
23.7%
-100000
20.0%
297196
19.4%
860996
12.2%
146467
 
9.3%
432813
 
6.6%
713009
 
2.6%
912555
 
2.5%
57256
 
1.5%
36194
 
1.2%

performance_title
Text

Missing 

Distinct394
Distinct (%)0.8%
Missing2215
Missing (%)4.4%
Memory size781.2 KiB
2025-11-02T20:43:28.219592image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/

Length

Max length98
Median length56
Mean length21.73223815
Min length1

Characters and Unicode

Total characters1038475
Distinct characters94
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12 ?
Unique (%)< 0.1%

Sample

1st rowEugene Onegin
2nd rowVirgin Money Fireworks Concert
3rd rowBlak Whyte Gray
4th rowThe Outrun
5th rowPhilharmonia Orchestra Residency: Fire in my mouth
ValueCountFrequency (%)
the8886
 
5.5%
6111
 
3.8%
orchestra6039
 
3.7%
of3375
 
2.1%
symphony3227
 
2.0%
residency2542
 
1.6%
opening2425
 
1.5%
concert2168
 
1.3%
outrun1778
 
1.1%
philharmonia1526
 
0.9%
Other values (836)124241
76.5%
2025-11-02T20:43:28.758619image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
114558
 
11.0%
e95910
 
9.2%
a68230
 
6.6%
r65993
 
6.4%
n62363
 
6.0%
i58819
 
5.7%
o54844
 
5.3%
t49436
 
4.8%
s45108
 
4.3%
h42231
 
4.1%
Other values (84)380983
36.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)1038475
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
114558
 
11.0%
e95910
 
9.2%
a68230
 
6.6%
r65993
 
6.4%
n62363
 
6.0%
i58819
 
5.7%
o54844
 
5.3%
t49436
 
4.8%
s45108
 
4.3%
h42231
 
4.1%
Other values (84)380983
36.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1038475
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
114558
 
11.0%
e95910
 
9.2%
a68230
 
6.6%
r65993
 
6.4%
n62363
 
6.0%
i58819
 
5.7%
o54844
 
5.3%
t49436
 
4.8%
s45108
 
4.3%
h42231
 
4.1%
Other values (84)380983
36.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1038475
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
114558
 
11.0%
e95910
 
9.2%
a68230
 
6.6%
r65993
 
6.4%
n62363
 
6.0%
i58819
 
5.7%
o54844
 
5.3%
t49436
 
4.8%
s45108
 
4.3%
h42231
 
4.1%
Other values (84)380983
36.7%
Distinct33
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size781.2 KiB
2025-11-02T20:43:28.960574image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/

Length

Max length34
Median length32
Mean length13.5545
Min length7

Characters and Unicode

Total characters677725
Distinct characters45
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowFestival Theatre
2nd rowPrinces Street Gardens
3rd rowThe Lyceum
4th rowChurch Hill Theatre
5th rowUsher Hall
ValueCountFrequency (%)
hall23550
20.8%
the16473
14.6%
usher14799
13.1%
theatre12163
10.7%
queen's8343
 
7.4%
festival6874
 
6.1%
lyceum4547
 
4.0%
edinburgh3406
 
3.0%
playhouse3406
 
3.0%
church2620
 
2.3%
Other values (50)17010
15.0%
2025-11-02T20:43:29.281663image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e95036
14.0%
l65168
 
9.6%
63191
 
9.3%
h56588
 
8.3%
a49715
 
7.3%
s39162
 
5.8%
r37780
 
5.6%
H29336
 
4.3%
T28870
 
4.3%
u27019
 
4.0%
Other values (35)185860
27.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)677725
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e95036
14.0%
l65168
 
9.6%
63191
 
9.3%
h56588
 
8.3%
a49715
 
7.3%
s39162
 
5.8%
r37780
 
5.6%
H29336
 
4.3%
T28870
 
4.3%
u27019
 
4.0%
Other values (35)185860
27.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)677725
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e95036
14.0%
l65168
 
9.6%
63191
 
9.3%
h56588
 
8.3%
a49715
 
7.3%
s39162
 
5.8%
r37780
 
5.6%
H29336
 
4.3%
T28870
 
4.3%
u27019
 
4.0%
Other values (35)185860
27.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)677725
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e95036
14.0%
l65168
 
9.6%
63191
 
9.3%
h56588
 
8.3%
a49715
 
7.3%
s39162
 
5.8%
r37780
 
5.6%
H29336
 
4.3%
T28870
 
4.3%
u27019
 
4.0%
Other values (35)185860
27.4%
Distinct20
Distinct (%)< 0.1%
Missing301
Missing (%)0.6%
Memory size781.2 KiB
2025-11-02T20:43:29.453578image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/

Length

Max length20
Median length19
Mean length11.72772088
Min length4

Characters and Unicode

Total characters582856
Distinct characters38
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOpera
2nd rowFireworks
3rd rowDance
4th row04. Theatre
5th row01. Classical Music
ValueCountFrequency (%)
classical21348
23.0%
music15070
16.2%
0110520
11.3%
theatre9811
10.6%
opera4824
 
5.2%
024550
 
4.9%
other4550
 
4.9%
044275
 
4.6%
dance3780
 
4.1%
052698
 
2.9%
Other values (15)11541
12.4%
2025-11-02T20:43:29.767956image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a63492
 
10.9%
s60357
 
10.4%
l43616
 
7.5%
43268
 
7.4%
c41011
 
7.0%
i39865
 
6.8%
e39115
 
6.7%
.26683
 
4.6%
025539
 
4.4%
r23820
 
4.1%
Other values (28)176090
30.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)582856
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a63492
 
10.9%
s60357
 
10.4%
l43616
 
7.5%
43268
 
7.4%
c41011
 
7.0%
i39865
 
6.8%
e39115
 
6.7%
.26683
 
4.6%
025539
 
4.4%
r23820
 
4.1%
Other values (28)176090
30.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)582856
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a63492
 
10.9%
s60357
 
10.4%
l43616
 
7.5%
43268
 
7.4%
c41011
 
7.0%
i39865
 
6.8%
e39115
 
6.7%
.26683
 
4.6%
025539
 
4.4%
r23820
 
4.1%
Other values (28)176090
30.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)582856
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a63492
 
10.9%
s60357
 
10.4%
l43616
 
7.5%
43268
 
7.4%
c41011
 
7.0%
i39865
 
6.8%
e39115
 
6.7%
.26683
 
4.6%
025539
 
4.4%
r23820
 
4.1%
Other values (28)176090
30.2%
Distinct929
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size781.2 KiB
2025-11-02T20:43:30.079850image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters950000
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique191 ?
Unique (%)0.4%

Sample

1st row2019-08-17 19:15:00
2nd row2017-08-28 21:00:00
3rd row2017-08-19 14:30:00
4th row2024-08-21 20:00:00
5th row2024-08-21 20:00:00
ValueCountFrequency (%)
20:00:0012765
 
12.8%
19:30:0010372
 
10.4%
11:00:007409
 
7.4%
19:00:003963
 
4.0%
18:00:002436
 
2.4%
2024-08-251987
 
2.0%
15:00:001817
 
1.8%
2024-08-181689
 
1.7%
2024-08-241602
 
1.6%
2024-08-171466
 
1.5%
Other values (163)54494
54.5%
2025-11-02T20:43:30.536524image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0311290
32.8%
2115433
 
12.2%
-100000
 
10.5%
:100000
 
10.5%
190258
 
9.5%
863567
 
6.7%
50000
 
5.3%
436886
 
3.9%
928887
 
3.0%
320680
 
2.2%
Other values (3)32999
 
3.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)950000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0311290
32.8%
2115433
 
12.2%
-100000
 
10.5%
:100000
 
10.5%
190258
 
9.5%
863567
 
6.7%
50000
 
5.3%
436886
 
3.9%
928887
 
3.0%
320680
 
2.2%
Other values (3)32999
 
3.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)950000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0311290
32.8%
2115433
 
12.2%
-100000
 
10.5%
:100000
 
10.5%
190258
 
9.5%
863567
 
6.7%
50000
 
5.3%
436886
 
3.9%
928887
 
3.0%
320680
 
2.2%
Other values (3)32999
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)950000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0311290
32.8%
2115433
 
12.2%
-100000
 
10.5%
:100000
 
10.5%
190258
 
9.5%
863567
 
6.7%
50000
 
5.3%
436886
 
3.9%
928887
 
3.0%
320680
 
2.2%
Other values (3)32999
 
3.5%

end_time
Text

Missing 

Distinct435
Distinct (%)2.9%
Missing35221
Missing (%)70.4%
Memory size781.2 KiB
2025-11-02T20:43:30.924662image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters280801
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique61 ?
Unique (%)0.4%

Sample

1st row2019-08-17 22:15:00
2nd row2018-08-24 23:45:00
3rd row2018-08-18 16:20:00
4th row2018-08-21 21:45:00
5th row2019-08-10 20:40:00
ValueCountFrequency (%)
21:30:001927
 
6.5%
22:00:001712
 
5.8%
12:45:001063
 
3.6%
22:15:00934
 
3.2%
21:45:00831
 
2.8%
12:50:00688
 
2.3%
21:15:00615
 
2.1%
22:20:00546
 
1.8%
2019-08-15509
 
1.7%
2019-08-24505
 
1.7%
Other values (108)20228
68.4%
2025-11-02T20:43:31.421365image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
078634
28.0%
238586
13.7%
134183
12.2%
-29558
 
10.5%
:29558
 
10.5%
822607
 
8.1%
14779
 
5.3%
99954
 
3.5%
58478
 
3.0%
35147
 
1.8%
Other values (3)9317
 
3.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)280801
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
078634
28.0%
238586
13.7%
134183
12.2%
-29558
 
10.5%
:29558
 
10.5%
822607
 
8.1%
14779
 
5.3%
99954
 
3.5%
58478
 
3.0%
35147
 
1.8%
Other values (3)9317
 
3.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)280801
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
078634
28.0%
238586
13.7%
134183
12.2%
-29558
 
10.5%
:29558
 
10.5%
822607
 
8.1%
14779
 
5.3%
99954
 
3.5%
58478
 
3.0%
35147
 
1.8%
Other values (3)9317
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)280801
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
078634
28.0%
238586
13.7%
134183
12.2%
-29558
 
10.5%
:29558
 
10.5%
822607
 
8.1%
14779
 
5.3%
99954
 
3.5%
58478
 
3.0%
35147
 
1.8%
Other values (3)9317
 
3.3%

ticket_count
Real number (ℝ)

Skewed 

Distinct219
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.1167
Minimum-22
Maximum4948
Zeros15
Zeros (%)< 0.1%
Negative40
Negative (%)0.1%
Memory size781.2 KiB
2025-11-02T20:43:31.577710image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/

Quantile statistics

Minimum-22
5-th percentile1
Q12
median2
Q36
95-th percentile29
Maximum4948
Range4970
Interquartile range (IQR)4

Descriptive statistics

Standard deviation90.32755937
Coefficient of variation (CV)8.928559646
Kurtosis1298.197768
Mean10.1167
Median Absolute Deviation (MAD)1
Skewness33.44284555
Sum505835
Variance8159.067982
MonotonicityNot monotonic
2025-11-02T20:43:31.734538image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
216730
33.5%
112252
24.5%
43265
 
6.5%
33068
 
6.1%
61621
 
3.2%
81152
 
2.3%
51024
 
2.0%
101021
 
2.0%
12884
 
1.8%
7676
 
1.4%
Other values (209)8307
16.6%
ValueCountFrequency (%)
-222
< 0.1%
-162
< 0.1%
-121
< 0.1%
-112
< 0.1%
-91
< 0.1%
ValueCountFrequency (%)
49481
< 0.1%
43081
< 0.1%
41702
< 0.1%
40562
< 0.1%
40311
< 0.1%

postcode
Text

Missing 

Distinct4604
Distinct (%)9.9%
Missing3635
Missing (%)7.3%
Memory size781.2 KiB
2025-11-02T20:43:32.208104image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/

Length

Max length9
Median length4
Mean length4.704561631
Min length1

Characters and Unicode

Total characters218127
Distinct characters63
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2045 ?
Unique (%)4.4%

Sample

1st rowIV2
2nd rowEH32
3rd rowEH5
4th rowEH6 7
5th rowSE17 3
ValueCountFrequency (%)
53685
 
5.1%
13540
 
4.9%
eh33217
 
4.5%
eh103075
 
4.3%
63037
 
4.2%
eh92858
 
4.0%
42689
 
3.8%
22543
 
3.5%
eh42438
 
3.4%
92122
 
3.0%
Other values (2160)42469
59.3%
2025-11-02T20:43:32.830731image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
36810
16.9%
E29657
13.6%
H29059
13.3%
125308
11.6%
210205
 
4.7%
410148
 
4.7%
39920
 
4.5%
68708
 
4.0%
58179
 
3.7%
07396
 
3.4%
Other values (53)42737
19.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)218127
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
36810
16.9%
E29657
13.6%
H29059
13.3%
125308
11.6%
210205
 
4.7%
410148
 
4.7%
39920
 
4.5%
68708
 
4.0%
58179
 
3.7%
07396
 
3.4%
Other values (53)42737
19.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)218127
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
36810
16.9%
E29657
13.6%
H29059
13.3%
125308
11.6%
210205
 
4.7%
410148
 
4.7%
39920
 
4.5%
68708
 
4.0%
58179
 
3.7%
07396
 
3.4%
Other values (53)42737
19.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)218127
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
36810
16.9%
E29657
13.6%
H29059
13.3%
125308
11.6%
210205
 
4.7%
410148
 
4.7%
39920
 
4.5%
68708
 
4.0%
58179
 
3.7%
07396
 
3.4%
Other values (53)42737
19.6%

capacity
Real number (ℝ)

Missing 

Distinct25
Distinct (%)0.1%
Missing27015
Missing (%)54.0%
Infinite0
Infinite (%)0.0%
Mean1848.42754
Minimum25
Maximum24672
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size781.2 KiB
2025-11-02T20:43:32.967048image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/

Quantile statistics

Minimum25
5-th percentile190
Q1980
median1887
Q32376
95-th percentile3019
Maximum24672
Range24647
Interquartile range (IQR)1396

Descriptive statistics

Standard deviation2149.141491
Coefficient of variation (CV)1.162686362
Kurtosis47.98935712
Mean1848.42754
Median Absolute Deviation (MAD)598
Skewness6.052524792
Sum42486107
Variance4618809.149
MonotonicityNot monotonic
2025-11-02T20:43:33.096070image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
23766950
 
13.9%
9803722
 
7.4%
18873064
 
6.1%
12892190
 
4.4%
6381897
 
3.8%
30191115
 
2.2%
347691
 
1.4%
170602
 
1.2%
398572
 
1.1%
1400532
 
1.1%
Other values (15)1650
 
3.3%
(Missing)27015
54.0%
ValueCountFrequency (%)
25141
0.3%
402
 
< 0.1%
45141
0.3%
60119
0.2%
12017
 
< 0.1%
ValueCountFrequency (%)
2467267
 
0.1%
1500069
 
0.1%
12750325
 
0.7%
800092
 
0.2%
30191115
2.2%

venue_address
Text

Missing 

Distinct24
Distinct (%)0.1%
Missing28927
Missing (%)57.9%
Memory size781.2 KiB
2025-11-02T20:43:33.370645image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/

Length

Max length58
Median length38
Mean length32.55454847
Min length9

Characters and Unicode

Total characters686022
Distinct characters55
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row13-29 Nicolson St, Edinburgh EH8 9FT
2nd rowPrinces Street, Edinburgh
3rd row30b Grindlay St, Edinburgh EH3 9AX
4th rowClerk St, Newington, Edinburgh EH8 9JG
5th rowLothian Rd, Edinburgh EH1 2EA
ValueCountFrequency (%)
edinburgh21073
18.2%
st10353
 
8.9%
eh18645
 
7.5%
rd7875
 
6.8%
eh86722
 
5.8%
lothian6649
 
5.7%
2ea6580
 
5.7%
eh33858
 
3.3%
clerk3407
 
2.9%
9jg3407
 
2.9%
Other values (66)37442
32.3%
2025-11-02T20:43:33.748064image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
94938
 
13.8%
E49543
 
7.2%
n44378
 
6.5%
i39579
 
5.8%
d33135
 
4.8%
r31385
 
4.6%
h28290
 
4.1%
g25529
 
3.7%
,24274
 
3.5%
t23192
 
3.4%
Other values (45)291779
42.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)686022
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
94938
 
13.8%
E49543
 
7.2%
n44378
 
6.5%
i39579
 
5.8%
d33135
 
4.8%
r31385
 
4.6%
h28290
 
4.1%
g25529
 
3.7%
,24274
 
3.5%
t23192
 
3.4%
Other values (45)291779
42.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)686022
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
94938
 
13.8%
E49543
 
7.2%
n44378
 
6.5%
i39579
 
5.8%
d33135
 
4.8%
r31385
 
4.6%
h28290
 
4.1%
g25529
 
3.7%
,24274
 
3.5%
t23192
 
3.4%
Other values (45)291779
42.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)686022
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
94938
 
13.8%
E49543
 
7.2%
n44378
 
6.5%
i39579
 
5.8%
d33135
 
4.8%
r31385
 
4.6%
h28290
 
4.1%
g25529
 
3.7%
,24274
 
3.5%
t23192
 
3.4%
Other values (45)291779
42.5%

latitude
Real number (ℝ)

Missing 

Distinct31
Distinct (%)0.1%
Missing1904
Missing (%)3.8%
Infinite0
Infinite (%)0.0%
Mean55.94620388
Minimum55.901305
Maximum55.975835
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size781.2 KiB
2025-11-02T20:43:33.870428image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/

Quantile statistics

Minimum55.901305
5-th percentile55.932678
Q155.942105
median55.946994
Q355.947239
95-th percentile55.957059
Maximum55.975835
Range0.07453
Interquartile range (IQR)0.005134

Descriptive statistics

Standard deviation0.006130014876
Coefficient of variation (CV)0.0001095698091
Kurtosis10.08293111
Mean55.94620388
Median Absolute Deviation (MAD)0.000245
Skewness0.3189445554
Sum2690788.622
Variance3.757708239 × 10-5
MonotonicityNot monotonic
2025-11-02T20:43:34.002229image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
55.94723914799
29.6%
55.9469036805
13.6%
55.9416334936
 
9.9%
55.9469944395
 
8.8%
55.9414373407
 
6.8%
55.9326782479
 
5.0%
55.9570592330
 
4.7%
55.9421052199
 
4.4%
55.9491422000
 
4.0%
55.9462141323
 
2.6%
Other values (21)3423
 
6.8%
(Missing)1904
 
3.8%
ValueCountFrequency (%)
55.90130582
 
0.2%
55.92973116
 
< 0.1%
55.9326782479
5.0%
55.932703141
 
0.3%
55.93908854
 
0.1%
ValueCountFrequency (%)
55.975835535
 
1.1%
55.9757241
 
< 0.1%
55.9570592330
4.7%
55.9570421076
2.2%
55.95566923
 
< 0.1%

longitude
Real number (ℝ)

Missing 

Distinct30
Distinct (%)0.1%
Missing1904
Missing (%)3.8%
Infinite0
Infinite (%)0.0%
Mean-3.196048109
Minimum-3.421135
Maximum-3.133686
Zeros0
Zeros (%)0.0%
Negative48096
Negative (%)96.2%
Memory size781.2 KiB
2025-11-02T20:43:34.130139image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/

Quantile statistics

Minimum-3.421135
5-th percentile-3.209666
Q1-3.205368
median-3.203251
Q3-3.18545
95-th percentile-3.181739
Maximum-3.133686
Range0.287449
Interquartile range (IQR)0.019918

Descriptive statistics

Standard deviation0.01411397138
Coefficient of variation (CV)-0.00441606975
Kurtosis107.87283
Mean-3.196048109
Median Absolute Deviation (MAD)0.006415
Skewness-6.78994303
Sum-153717.1299
Variance0.000199204188
MonotonicityNot monotonic
2025-11-02T20:43:34.257818image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
-3.20536814799
29.6%
-3.1860076805
13.6%
-3.1817394936
 
9.9%
-3.2043984395
 
8.8%
-3.181773407
 
6.8%
-3.2096662479
 
5.0%
-3.185452330
 
4.7%
-3.2032512199
 
4.4%
-3.1949342000
 
4.0%
-3.1871461323
 
2.6%
Other values (20)3423
 
6.8%
(Missing)1904
 
3.8%
ValueCountFrequency (%)
-3.42113582
0.2%
-3.23234854
 
0.1%
-3.23222214
 
< 0.1%
-3.2107022
 
< 0.1%
-3.209742141
0.3%
ValueCountFrequency (%)
-3.13368616
 
< 0.1%
-3.180622536
 
1.1%
-3.1817394936
9.9%
-3.181773407
6.8%
-3.1850731076
 
2.2%

duration_mins
Real number (ℝ)

Missing 

Distinct37
Distinct (%)0.1%
Missing8442
Missing (%)16.9%
Infinite0
Infinite (%)0.0%
Mean111.9885461
Minimum0
Maximum405
Zeros22
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size781.2 KiB
2025-11-02T20:43:34.395201image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile60
Q190
median105
Q3120
95-th percentile210
Maximum405
Range405
Interquartile range (IQR)30

Descriptive statistics

Standard deviation44.74289517
Coefficient of variation (CV)0.3995309942
Kurtosis7.309162269
Mean111.9885461
Median Absolute Deviation (MAD)15
Skewness1.989315015
Sum4654020
Variance2001.926668
MonotonicityNot monotonic
2025-11-02T20:43:34.542648image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
909258
18.5%
1054861
9.7%
1204392
8.8%
602653
 
5.3%
752401
 
4.8%
1102182
 
4.4%
1802003
 
4.0%
701957
 
3.9%
2101574
 
3.1%
1501323
 
2.6%
Other values (27)8954
17.9%
(Missing)8442
16.9%
ValueCountFrequency (%)
022
 
< 0.1%
30339
0.7%
4035
 
0.1%
4529
 
0.1%
50372
0.7%
ValueCountFrequency (%)
40525
 
0.1%
385137
0.3%
34523
 
< 0.1%
330140
0.3%
28552
 
0.1%

transaction_datetime
Text

Missing 

Distinct20403
Distinct (%)76.4%
Missing23299
Missing (%)46.6%
Memory size781.2 KiB
2025-11-02T20:43:34.890151image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters507319
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique17558 ?
Unique (%)65.8%

Sample

1st row2024-05-19 20:34:59
2nd row2024-07-31 00:41:04
3rd row2024-05-26 11:59:07
4th row2024-08-05 15:24:09
5th row2024-03-21 12:07:59
ValueCountFrequency (%)
2024-03-212506
 
4.7%
2024-03-192344
 
4.4%
2024-03-18997
 
1.9%
2024-03-15588
 
1.1%
2024-08-06443
 
0.8%
2024-03-22412
 
0.8%
2024-08-02406
 
0.8%
2024-08-05405
 
0.8%
2024-03-25400
 
0.7%
2024-08-03400
 
0.7%
Other values (17022)44501
83.3%
2025-11-02T20:43:35.355651image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
290071
17.8%
081025
16.0%
-53402
10.5%
:53402
10.5%
152857
10.4%
446566
9.2%
329899
 
5.9%
26701
 
5.3%
519861
 
3.9%
817380
 
3.4%
Other values (3)36155
7.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)507319
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
290071
17.8%
081025
16.0%
-53402
10.5%
:53402
10.5%
152857
10.4%
446566
9.2%
329899
 
5.9%
26701
 
5.3%
519861
 
3.9%
817380
 
3.4%
Other values (3)36155
7.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)507319
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
290071
17.8%
081025
16.0%
-53402
10.5%
:53402
10.5%
152857
10.4%
446566
9.2%
329899
 
5.9%
26701
 
5.3%
519861
 
3.9%
817380
 
3.4%
Other values (3)36155
7.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)507319
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
290071
17.8%
081025
16.0%
-53402
10.5%
:53402
10.5%
152857
10.4%
446566
9.2%
329899
 
5.9%
26701
 
5.3%
519861
 
3.9%
817380
 
3.4%
Other values (3)36155
7.1%

audience_segment
Text

Missing 

Distinct7
Distinct (%)< 0.1%
Missing24378
Missing (%)48.8%
Memory size781.2 KiB
2025-11-02T20:43:35.514557image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/

Length

Max length15
Median length5
Mean length6.958317071
Min length5

Characters and Unicode

Total characters178286
Distinct characters28
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPlays/Drama
2nd rowMusic
3rd rowOther Artforms
4th rowChildren/Family
5th rowMusic
ValueCountFrequency (%)
music16357
58.3%
plays/drama3712
 
13.2%
dance2290
 
8.2%
other1203
 
4.3%
artforms1203
 
4.3%
outdoor1144
 
4.1%
arts1144
 
4.1%
children/family825
 
2.9%
not91
 
0.3%
applicable91
 
0.3%
2025-11-02T20:43:35.762788image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
s22416
12.6%
c18738
10.5%
i18098
10.2%
u17501
9.8%
M16357
 
9.2%
a14342
 
8.0%
r10434
 
5.9%
D6002
 
3.4%
m5740
 
3.2%
l5544
 
3.1%
Other values (18)43114
24.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)178286
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
s22416
12.6%
c18738
10.5%
i18098
10.2%
u17501
9.8%
M16357
 
9.2%
a14342
 
8.0%
r10434
 
5.9%
D6002
 
3.4%
m5740
 
3.2%
l5544
 
3.1%
Other values (18)43114
24.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)178286
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
s22416
12.6%
c18738
10.5%
i18098
10.2%
u17501
9.8%
M16357
 
9.2%
a14342
 
8.0%
r10434
 
5.9%
D6002
 
3.4%
m5740
 
3.2%
l5544
 
3.1%
Other values (18)43114
24.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)178286
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
s22416
12.6%
c18738
10.5%
i18098
10.2%
u17501
9.8%
M16357
 
9.2%
a14342
 
8.0%
r10434
 
5.9%
D6002
 
3.4%
m5740
 
3.2%
l5544
 
3.1%
Other values (18)43114
24.2%

audience_subsegment
Text

Missing 

Distinct22
Distinct (%)0.1%
Missing24378
Missing (%)48.8%
Memory size781.2 KiB
2025-11-02T20:43:35.955808image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/

Length

Max length23
Median length18
Mean length13.59804075
Min length5

Characters and Unicode

Total characters348409
Distinct characters45
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDrama New Writing
2nd rowContemporary Classical
3rd rowOther Talks
4th rowC&F Plays/Drama
5th rowWorld Music
ValueCountFrequency (%)
5371
 
9.6%
opera3556
 
6.4%
recitals3537
 
6.3%
chamber3537
 
6.3%
orchestral3449
 
6.2%
other2752
 
4.9%
drama2669
 
4.8%
new2669
 
4.8%
writing2669
 
4.8%
contemporary2646
 
4.7%
Other values (28)23071
41.3%
2025-11-02T20:43:36.295743image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
r33871
 
9.7%
a33689
 
9.7%
30304
 
8.7%
e27577
 
7.9%
t18645
 
5.4%
i15523
 
4.5%
c15168
 
4.4%
s14436
 
4.1%
l14256
 
4.1%
o13729
 
3.9%
Other values (35)131211
37.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)348409
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r33871
 
9.7%
a33689
 
9.7%
30304
 
8.7%
e27577
 
7.9%
t18645
 
5.4%
i15523
 
4.5%
c15168
 
4.4%
s14436
 
4.1%
l14256
 
4.1%
o13729
 
3.9%
Other values (35)131211
37.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)348409
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r33871
 
9.7%
a33689
 
9.7%
30304
 
8.7%
e27577
 
7.9%
t18645
 
5.4%
i15523
 
4.5%
c15168
 
4.4%
s14436
 
4.1%
l14256
 
4.1%
o13729
 
3.9%
Other values (35)131211
37.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)348409
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r33871
 
9.7%
a33689
 
9.7%
30304
 
8.7%
e27577
 
7.9%
t18645
 
5.4%
i15523
 
4.5%
c15168
 
4.4%
s14436
 
4.1%
l14256
 
4.1%
o13729
 
3.9%
Other values (35)131211
37.7%

membership_type
Text

Missing 

Distinct47
Distinct (%)0.7%
Missing43695
Missing (%)87.4%
Memory size781.2 KiB
2025-11-02T20:43:36.471720image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/

Length

Max length171
Median length99
Mean length23.1998414
Min length17

Characters and Unicode

Total characters146275
Distinct characters45
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)0.1%

Sample

1st rowFriend Membership
2nd rowAmbassador Membership - Monthly
3rd rowFriend Membership
4th rowGold Ambassador Membership
5th rowFriend Membership
ValueCountFrequency (%)
membership5873
31.8%
friend4487
24.3%
ambassador1275
 
6.9%
silver1213
 
6.6%
1011
 
5.5%
monthly896
 
4.9%
gold716
 
3.9%
young659
 
3.6%
musician's659
 
3.6%
pass576
 
3.1%
Other values (24)1077
 
5.8%
2025-11-02T20:43:36.814867image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e17903
12.2%
i13399
 
9.2%
r13052
 
8.9%
12142
 
8.3%
s11282
 
7.7%
M7428
 
5.1%
m7163
 
4.9%
b7148
 
4.9%
h6942
 
4.7%
n6794
 
4.6%
Other values (35)43022
29.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)146275
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e17903
12.2%
i13399
 
9.2%
r13052
 
8.9%
12142
 
8.3%
s11282
 
7.7%
M7428
 
5.1%
m7163
 
4.9%
b7148
 
4.9%
h6942
 
4.7%
n6794
 
4.6%
Other values (35)43022
29.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)146275
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e17903
12.2%
i13399
 
9.2%
r13052
 
8.9%
12142
 
8.3%
s11282
 
7.7%
M7428
 
5.1%
m7163
 
4.9%
b7148
 
4.9%
h6942
 
4.7%
n6794
 
4.6%
Other values (35)43022
29.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)146275
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e17903
12.2%
i13399
 
9.2%
r13052
 
8.9%
12142
 
8.3%
s11282
 
7.7%
M7428
 
5.1%
m7163
 
4.9%
b7148
 
4.9%
h6942
 
4.7%
n6794
 
4.6%
Other values (35)43022
29.4%

has_negative_tickets
Real number (ℝ)

Missing  Skewed  Zeros 

Distinct2
Distinct (%)< 0.1%
Missing28916
Missing (%)57.8%
Infinite0
Infinite (%)0.0%
Mean0.001897173212
Minimum0
Maximum1
Zeros21044
Zeros (%)42.1%
Negative0
Negative (%)0.0%
Memory size781.2 KiB
2025-11-02T20:43:36.908933image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.04351624709
Coefficient of variation (CV)22.93741384
Kurtosis522.2260213
Mean0.001897173212
Median Absolute Deviation (MAD)0
Skewness22.89490082
Sum40
Variance0.001893663761
MonotonicityNot monotonic
2025-11-02T20:43:36.998912image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
021044
42.1%
140
 
0.1%
(Missing)28916
57.8%
ValueCountFrequency (%)
021044
42.1%
140
 
0.1%
ValueCountFrequency (%)
140
 
0.1%
021044
42.1%
Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size781.2 KiB
2025-11-02T20:43:37.154576image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/

Length

Max length17
Median length17
Mean length16.70796
Min length10

Characters and Unicode

Total characters835398
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEIFData_2017-2019
2nd rowEIFData_2017-2019
3rd rowEIFData_2017-2019
4th rowTransactions_2024
5th rowTransactions_2024
ValueCountFrequency (%)
transactions_202426701
53.4%
eifdata_2017-201921084
42.2%
basic_20191914
 
3.8%
perevent_2019301
 
0.6%
2025-11-02T20:43:37.435190image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
297785
 
11.7%
a97484
 
11.7%
071084
 
8.5%
s55316
 
6.6%
n53703
 
6.4%
_50000
 
6.0%
t48086
 
5.8%
144383
 
5.3%
c28615
 
3.4%
i28615
 
3.4%
Other values (15)260327
31.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)835398
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
297785
 
11.7%
a97484
 
11.7%
071084
 
8.5%
s55316
 
6.6%
n53703
 
6.4%
_50000
 
6.0%
t48086
 
5.8%
144383
 
5.3%
c28615
 
3.4%
i28615
 
3.4%
Other values (15)260327
31.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)835398
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
297785
 
11.7%
a97484
 
11.7%
071084
 
8.5%
s55316
 
6.6%
n53703
 
6.4%
_50000
 
6.0%
t48086
 
5.8%
144383
 
5.3%
c28615
 
3.4%
i28615
 
3.4%
Other values (15)260327
31.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)835398
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
297785
 
11.7%
a97484
 
11.7%
071084
 
8.5%
s55316
 
6.6%
n53703
 
6.4%
_50000
 
6.0%
t48086
 
5.8%
144383
 
5.3%
c28615
 
3.4%
i28615
 
3.4%
Other values (15)260327
31.2%

year
Real number (ℝ)

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2021.22206
Minimum2017
Maximum2024
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size781.2 KiB
2025-11-02T20:43:37.516990image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/

Quantile statistics

Minimum2017
5-th percentile2017
Q12018
median2024
Q32024
95-th percentile2024
Maximum2024
Range7
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.030223259
Coefficient of variation (CV)0.001499203536
Kurtosis-1.80389157
Mean2021.22206
Median Absolute Deviation (MAD)0
Skewness-0.2458303365
Sum101061103
Variance9.182253001
MonotonicityNot monotonic
2025-11-02T20:43:37.612751image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/
Histogram with fixed size bins (bins=4)
ValueCountFrequency (%)
202426701
53.4%
20198924
 
17.8%
20178027
 
16.1%
20186348
 
12.7%
ValueCountFrequency (%)
20178027
 
16.1%
20186348
 
12.7%
20198924
 
17.8%
202426701
53.4%
ValueCountFrequency (%)
202426701
53.4%
20198924
 
17.8%
20186348
 
12.7%
20178027
 
16.1%

month
Real number (ℝ)

Skewed 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.99754
Minimum7
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size781.2 KiB
2025-11-02T20:43:37.711703image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile8
Q18
median8
Q38
95-th percentile8
Maximum8
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.04953783887
Coefficient of variation (CV)0.006194134555
Kurtosis401.5468047
Mean7.99754
Median Absolute Deviation (MAD)0
Skewness-20.08807464
Sum399877
Variance0.00245399748
MonotonicityNot monotonic
2025-11-02T20:43:37.800768image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
849877
99.8%
7123
 
0.2%
ValueCountFrequency (%)
7123
 
0.2%
849877
99.8%
ValueCountFrequency (%)
849877
99.8%
7123
 
0.2%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size781.2 KiB
2025-11-02T20:43:37.911362image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/

Length

Max length6
Median length6
Mean length5.99508
Min length4

Characters and Unicode

Total characters299754
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAugust
2nd rowAugust
3rd rowAugust
4th rowAugust
5th rowAugust
ValueCountFrequency (%)
august49877
99.8%
july123
 
0.2%
2025-11-02T20:43:38.144920image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
u99877
33.3%
A49877
16.6%
g49877
16.6%
s49877
16.6%
t49877
16.6%
J123
 
< 0.1%
l123
 
< 0.1%
y123
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)299754
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
u99877
33.3%
A49877
16.6%
g49877
16.6%
s49877
16.6%
t49877
16.6%
J123
 
< 0.1%
l123
 
< 0.1%
y123
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)299754
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
u99877
33.3%
A49877
16.6%
g49877
16.6%
s49877
16.6%
t49877
16.6%
J123
 
< 0.1%
l123
 
< 0.1%
y123
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)299754
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
u99877
33.3%
A49877
16.6%
g49877
16.6%
s49877
16.6%
t49877
16.6%
J123
 
< 0.1%
l123
 
< 0.1%
y123
 
< 0.1%